14
Oct
Computational Methods In Drug Discovery. Cheminformatics for example is the application of computer science to understanding and characterizing molecular attributes and chemical behavior of specific compounds. More specifically topics include innovative treatments for cancer selectivity modeling translational research allosteric modulation drug resistance modeling and novel methods in the domain of machine learning. Computational methods have emerged as an approach that is revolutionizing the face of drug discovery. Drug designing is an intense time consuming and an interdisciplinary venture.
Traditionally drugs were discovered by synthesizing compounds in a long-drawn-out and multi-step process. Computational approaches in target identification and drug discovery 1. With considerable rise in the number of drug targets computational methods such as protein structure prediction methods virtual high-throughput screening and docking methods have been used to accelerate the drug discovery process and are routinely used in academia and in the pharmaceutical industry. Structure-based approaches include ligand docking pharmacophore and ligand design methods. The efficiency of drug discovery and designing process can be increased by effective strategies given by computational methods. Chemoinformatic tools present a tremendous potential to advance in silico drug design.
This Virtual Issue showcases the synergy and complementary nature of these journals by highlighting publications that delineate the.
Understanding the interactions between proteins and ligands is crucial for the pharmaceutical industries. In this review we present an overview of these important computational methods platforms and successful applications in this field. Structure-based approaches include ligand docking pharmacophore and ligand design methods. Drug designing is an intense time consuming and an interdisciplinary venture. Over the past decades computational drug discovery methods such as molecular docking pharmacophore modeling and mapping de novo design molecular similarity calculation and. More specifically topics include innovative treatments for cancer selectivity modeling translational research allosteric modulation drug resistance modeling and novel methods in the domain of machine learning.